Shared-Memory Parallel Computation of Morse-Smale Complexes with Improved Accuracy
Shared-Memory Parallel Computation of Morse-Smale Complexes with Improved Accuracy
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DOI：
10.1109/tvcg.2018.2864848
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发表时间：
2019-01
影响因子：
5.2
通讯作者：
A. Gyulassy;P. Bremer;Valerio Pascucci
中科院分区：
文献类型：
--
作者：
A. Gyulassy;P. Bremer;Valerio Pascucci
拓扑技术已被证明是大规模科学数据分析和可视化的有力工具。特别是莫尔斯-斯莫尔复合体及其各种组成部分为鲁棒特征定义和计算提供了丰富的框架。因此，现在存在许多方法来并行计算大规模数据的摩尔斯-小复合体。然而，现有的技术是基于离散的概念，产生正确的拓扑结构，但已知会在结果几何中引入网格伪影。在这里，我们提出了一种新的方法，将并行流线计算与组合方法相结合，以构建高质量的离散莫尔斯-小复合体。除了对底层网格的方向保持不变外，该算法还允许用户使用高质量的几何图形有选择地构建特征子集。特别是，用户可以特别选择哪些上升/下降流形以更高的精度重建，将计算精力集中在对后续分析有影响的地方。这种方法比以前可行的计算更大数据的莫尔斯-小复合体具有显著的速度。我们使用来自不同科学领域的几个例子来演示和验证我们的方法，并评估我们方法的性能。
Topological techniques have proven to be a powerful tool in the analysis and visualization of large-scale scientific data. In particular, the Morse-Smale complex and its various components provide a rich framework for robust feature definition and computation. Consequently, there now exist a number of approaches to compute Morse-Smale complexes for large-scale data in parallel. However, existing techniques are based on discrete concepts which produce the correct topological structure but are known to introduce grid artifacts in the resulting geometry. Here, we present a new approach that combines parallel streamline computation with combinatorial methods to construct a high-quality discrete Morse-Smale complex. In addition to being invariant to the orientation of the underlying grid, this algorithm allows users to selectively build a subset of features using high-quality geometry. In particular, a user may specifically select which ascending/descending manifolds are reconstructed with improved accuracy, focusing computational effort where it matters for subsequent analysis. This approach computes Morse-Smale complexes for larger data than previously feasible with significant speedups. We demonstrate and validate our approach using several examples from a variety of different scientific domains, and evaluate the performance of our method.